Remote sensing of geometric shapes and locations of a target material has proven to be valuable in a number of fields, including for the study of geological formations. Conventional technologies include light detection and ranging (“lidar”), which is generally recognized as an “active” remote sensing technique that uses a single laser frequency to actively illuminate a target and determine range. Although a single frequency laser intensity value can offer some information about a target material's composition, identifying the chemical composition of the target material, such as specific mineralogy, cannot be accomplished by using a single frequency lidar instrument. A more complete spectral analysis of a target material requires multiple discrete frequencies to be simultaneously collected. Matching of separately obtained lidar information with hyperspectral data can require costly post-processing to perform resolution matching and illumination matching.
In addition, conventional spectral analysis techniques utilize spectral samples over multiple wavelengths of naturally occurring electromagnetic radiation, e.g., sunlight, to identify a target chemical composition. Such techniques are commonly referred to as “passive” remote sensing in that light is passively collected rather than actively directed at a target material. Spectral image analysis of passively collected data also requires considerable post-processing to extract meaningful results. Commercially available instruments cannot be dynamically modified during acquisition to collect maximum resolution of a target material based on real-time feedback from the instrument during acquisition.
Further, conventional spectral imaging techniques utilizing natural electromagnetic radiation are characterized by incomplete spectral coverage in the reflected infrared spectral region due to absorption of frequencies, e.g., 1400-1600 nm and 1800-2000 nm, from long transit distance through the atmosphere, e.g., several tens of kilometers. Using wide spectral bands, e.g., many tens of nanometers wide, is known to yield blurred spectral samples when inspecting the target material's spectral composition. Typically, many narrow bands are required, e.g., approximately 1 nm wide, to discretely determine chemical attributes of the target.
As such, a need exists to remotely sense target shape and target composition simultaneously across several hundred discrete wavelengths (˜1 nm wide) while minimizing costly post-processing to perform resolution matching and illumination matching.